DGT DOCS
  • 1. INTRODUCTION
    • 1.1 Executive Summary
    • 1.2 Why DGT
    • 1.3 Distributed Ledgers Technology
      • 1.3.1 Decentralization approach
      • 1.3.2 Consensus Mechanism
      • 1.3.3 Transactions
      • 1.3.4 Layered Blockchain Architecture
      • 1.3.5 Tokenomics
      • 1.3.6 Web 3 Paradigm
      • 1.3.7 Common Myths about Blockchain
    • 1.4 The DGT Overview
      • 1.4.1 Platform Approach
      • 1.4.2 DGT Functional Architecture
      • 1.4.3 Technology Roadmap
    • 1.5 How to create a Solution with DGT Networks
    • 1.6 Acknowledgments
  • 2. REAL WORLD APPLICATIONS
    • 2.1 Case-Based Approach
      • 2.1.1 DGT Mission
      • 2.1.2 The Methodology
      • 2.1.3 Case Selection
    • 2.2 Supply Chain and Vertical Integration
      • 2.2.1 Logistics Solution for Spare Parts Delivery
      • 2.2.2 DGT Based Solution for Coffee Chain Products
    • 2.3 Innovative Financial Services
      • 2.3.1 Crowdfunding Platform
      • 2.3.2 Real World Assets Tokenization
      • 2.3.3 Virtual Neobank over DGT Network
      • 2.3.4 DGT based NFT Marketplace
    • 2.4 Decentralized Green Energy Market
      • 2.4.1 Peer To Peer Energy Trading
      • 2.4.2 DGT based Carbon Offset Trading
    • 2.5 B2B2C Ecosystems and Horizontal Integration
      • 2.5.1 KYC and User Scoring
      • 2.5.2 Decentralized Marketing Attribution
      • 2.5.3 Case Decentralized Publishing Platform
      • 2.5.4 Value Ecosystem
    • 2.6 More Cases
  • 3. DGT ARCHITECTURE
    • 3.1 Scalable Architecture Design
      • 3.1.1 High Level Architecture
      • 3.1.2 DGT Approach
      • 3.1.3 Unique contribution
      • 3.1.4 Component Based Architecture
    • 3.2 Performance Metrics
    • 3.3 Network Architecture
      • 3.3.1 Nework Architecture in General
      • 3.3.2 Network Identification
      • 3.3.3 H-Net Architecture
      • 3.3.4 Transport Level
      • 3.3.5 Segments
      • 3.3.6 Static and Dynamic Topologies
      • 3.3.7 Cluster Formation
      • 3.3.8 Node Networking
      • 3.3.9 Permalinks Control Protocol
    • 3.4 Fault-Tolerant Architecture
      • 3.4.1 Introduction to Fault Tolerance
      • 3.4.2 F-BFT: The Hierarchical Consensus Mechanism
      • 3.4.3 Cluster Based Algorithms
      • 3.4.4 Arbitrator Security Scheme
      • 3.4.5 Heartbeat Protocol
      • 3.4.6 Oracles and Notaries
      • 3.4.7 DID & KYC
    • 3.5 Transactions and Performance
      • 3.5.1 Transaction Basics
      • 3.5.2 Transaction Processing
      • 3.5.3 Transaction and block signing
      • 3.5.4 Transaction Families
      • 3.5.5 Transaction Receipts
      • 3.5.6 Smart Transactions
      • 3.5.7 Private Transactions
      • 3.5.8 Multi signature
    • 3.6 Data-Centric Model
      • 3.6.1 Data layer overview
      • 3.6.2 Global State
      • 3.6.3 Genesis Record
      • 3.6.4 Sharding
      • 3.6.5 DAG Synchronization
    • 3.7 Cryptography and Security
      • 3.7.1 Security Architecture Approach
      • 3.7.2 Base Cryptography
      • 3.7.3 Permission Design
      • 3.7.4 Key Management
      • 3.7.5 Encryption and Decryption
      • 3.7.6 Secure Multi Party Computation
      • 3.7.7 Cryptographic Agility
      • DGTTECH_3.8.4 Gateway Nodes
    • 3.8 Interoperability
      • 3.8.1 Interoperability Approach
      • 3.8.2 Relay Chain Pattern
      • 3.8.3 Virtual Machine Compatibility
      • 3.8.4 Gateway Nodes
      • 3.8.5 Token Bridge
    • 3.9 DGT API and Consumer Apps
      • 3.9.1 Presentation Layer
      • 3.9.2 Application Architecture
    • 3.10 Technology Stack
    • REFERENCES
  • 4. TOKENIZATION AND PROCESSING
    • 4.1 Introduction to Tokenization
      • 4.1.1 DGT Universe
      • 4.1.2 Driving Digital Transformation with Tokens
      • 4.1.3 Real-World Tokenization
      • 4.1.4 Key Concepts and Definitions
    • 4.2 Foundations of Tokenization
      • 4.2.1 Definition and Evolution of Tokenization
      • 4.2.2 Tokenization in the Blockchain/DLT Space
      • 4.2.3 The Tokenization Process
      • 4.2.4 Tokenization on the DGT Platform
      • 4.2.5 Regulatory and Legal Aspects of Tokenization
      • 4.2.6 Typical Blockchain-Based Business Models
    • 4.3 The DEC Transaction Family
      • 4.3.1 DEC Transaction Family Overview
      • 4.3.2 DEC Token Features
      • 4.3.3 DEC Token Protocol
      • 4.3.4 DEC Account Design
      • 4.3.5 DEC Transaction Family Flow
      • 4.3.6 DEC Commands
      • 4.3.7 DEC Processing
      • 4.3.8 Payment Gateways
    • 4.4 Understanding Secondary Tokens
      • 4.4.1 The different types of tokens supported by DGT
      • 4.4.2 How secondary tokens are produced
  • 5. EXPLORING TOKENOMICS
    • 5.1 Introduction
      • 5.1.1 What does tokenomics mean?
      • 5.1.2 Goals of Building the Model for DGT Network
      • 5.1.3 Tokens vs Digital Money
      • 5.1.4 The Phenomenon of Cryptocurrency
      • 5.1.5 Basic Principles of Tokenomics
      • 5.1.6 AB2023 Model
    • 5.2 Node & User Growth
      • 5.2.1 Node Ecosystem
      • 5.2.2 User Growth and Retention Modeling
    • 5.3 Transactions
      • 5.3.1 Transaction Amount Components
      • 5.3.2 Shaping the Transaction Profile: A Three-pronged Approach
      • 5.3.3 Calculation of Transaction Number
    • 5.4 Network Performance Simulation
      • 5.4.1 Endogenous Model
      • 5.4.2 Network Entropy
      • 5.4.3 Network Utility
    • 5.5 Token Supply Model
      • 5.5.1 Introduction to Supply and Demand Dynamics
      • 5.5.2 Token distribution
      • 5.5.3 Supply Protocol
      • 5.5.4 Token Balance and Cumulative Supply
    • 5.6 Token Demand Model
      • 5.6.1 Node-Base Demand
      • 5.6.2 Transaction-Based Token Demand
      • 5.6.3 Staking Part Modeling
      • 5.6.4 Total Demand
    • 5.7 Token Price Simulation
      • 5.7.1 Nelson-Siegel-Svensson model
      • 5.7.2 The Price Model
    • 5.8 Decentralization Measurement
      • 5.8.1 Active Node Index
      • 5.8.2 Node Diversity in Hybrid Networks
      • 5.8.3 Token distribution
      • 5.8.4 Integral Calculation of Decentralization Metric
    • 5.9 Aggregated Metrics
      • 5.9.1 Transaction Throughput: Evaluating Network Performance and Scalability
      • 5.9.2 Market Capitalization: A Dimension of Valuation in Cryptocurrency
      • 5.9.3 Total Value Locked (TVL): A Spotlight on Network Engagement and Trust
  • 6. ADMINISTRATOR GUIDE
    • 6.1 Introduction
      • 6.1.1 Administrator Role
      • 6.1.2 Platform sourcing
      • 6.1.3 DGT Virtualization
      • 6.1.4 Using Pre-Built Virtual Machine Images
      • 6.1.5 Server Preparation
      • 6.1.6 OS Setup and initialization
    • 6.2 DGT CORE: Single Node Setup
      • 6.2.1 Launch the First DGT Node
      • 6.2.2 Dashboard setup
      • 6.2.3 Nodes Port Configuration
      • 6.2.4 Single Node Check
    • 6.3 DGT CORE: Setup Private/Public Network
      • 6.3.1 Network launch preparation
      • 6.3.2 A Virtual Cluster
      • 6.3.3 A Physical Network
      • 6.3.4 Attach node to Existing Network
    • 6.4 DGT Dashboard
    • 6.5 DGT CLI and base transaction families
    • 6.6 GARANASKA: Financial Processing
      • 6.6.1 Overview of DGT’s financial subsystem
      • 6.6.2 DEC emission
      • 6.6.3 Consortium account
      • 6.6.4 User accounts
      • 6.6.5 Payments
    • 6.7 Adjust DGT settings
      • 6.7.1 DGT Topology
      • 6.7.2 Manage local settings
    • 6.8 DGT Maintenance
      • 6.8.1 Stopping and Restarting the Platform
      • 6.8.2 Backing up Databases
      • 6.8.3 Network Performance
      • 6.8.4 Log & Monitoring
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  1. 5. EXPLORING TOKENOMICS
  2. 5.7 Token Price Simulation

5.7.1 Nelson-Siegel-Svensson model

Previous5.7 Token Price SimulationNext5.7.2 The Price Model

Last updated 1 year ago

The Nelson-Siegel-Svensson (NSS) model (Medvedev 2019) is an extended version of the Nelson-Siegel (NS) model (Annaert et al. 2013), commonly used to describe interest rate term structures or yield curves. A yield curve represents the relationship between the interest rate (or cost of borrowing) and the time to maturity of the debt for a given borrower in each currency.

The NSS model defines the instantaneous forward rate as a function of four components: a long-term factor, a short-term factor, a medium-term factor, and an over-demand factor:

Y(t)=β0+β1(1−e−t/τ1)t/τ1+β2(1−e−t/τ1t/τ1−e−t/τ1)+β3(1−e−t/τ2t/τ2−e−t/τ2)=Y0++Y1+Y2+Y3        (40)Y(t)= β_0 + \frac{β_1 (1-e^{-t/τ_1})}{t/τ_1 } + β_2\left( \frac{1-e^{-t/τ_1}}{t/τ_1} -e^{-t/τ_1} \right) + β_3 \left( \frac{1-e^{-t/τ_2}}{t/τ_2} - e^{-t/τ_2} \right) = Y_0+\\+Y_1+Y_2+Y_3 \ \ \ \ \ \ \ \ \tag{40}Y(t)=β0​+t/τ1​β1​(1−e−t/τ1​)​+β2​(t/τ1​1−e−t/τ1​​−e−t/τ1​)+β3​(t/τ2​1−e−t/τ2​​−e−t/τ2​)=Y0​++Y1​+Y2​+Y3​        (40)

Here's what each component represents:

o (Long term factor): This component, represented by , is the long-term interest rate or the level factor of the yield curve. It is a constant value that sets the asymptotic future interest rate as time tends to infinity.

o (Short term factor): This component captures the impact of short-term rates and is responsible for the initial steepness of the yield curve. The parameter sets the slope at time zero and represents a decay factor. The term introduces a decay effect, meaning the influence of this factor decreases as time progresses.

o (Medium term factor): This component models the hump in the yield curve. sets the size of the hump, while again is a decay factor. The term means the influence of this factor increases initially, reaches a peak, and then decreases.

o (Over-demand factor): This component, unique to the NSS model, captures any additional influences on the interest rate not covered by the other three components. sets the size of this influence, while acts as a decay factor. Like , the term introduces an increase, peak, and decrease effect.

The final yield curve is the sum of these four components, yielding a flexible model that can capture a variety of yield curve shapes.

Figure 91 Adjusted NSS Model over time